Bayesian econometric modelling of observational data for cost‐effectiveness analysis: establishing the value of negative pressure wound therapy in the healing of open surgical wounds
Pedro Saramago,
Karl Claxton,
Nicky J. Welton and
Marta Soares
Journal of the Royal Statistical Society Series A, 2020, vol. 183, issue 4, 1575-1593
Abstract:
In the absence of evidence from randomized controlled trials on the relative effectiveness of treatments, cost‐effectiveness analyses increasingly use observational data instead. Treatment assignment is not, however, randomized, and naive estimates of the treatment effect may be biased. To deal with this bias, one may need to adjust for observed and unobserved confounders. In this work we explore and discuss the challenges of these adjustment strategies within a case‐study of negative pressure wound therapy (NPWT) for the treatment of surgical wounds healing by secondary intention. We could not demonstrate that existing uncontrolled confounding affects NPWT effectiveness, and thus there was no evidence that NPWT was cost effective compared with standard dressings for the treatment of surgical wounds healing by secondary intention.
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1111/rssa.12596
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:183:y:2020:i:4:p:1575-1593
Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X
Access Statistics for this article
Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples
More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().